Genetic programming: new performance improving methods and applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{ekart:thesis,
  author =       "Aniko Ekart",
  title =        "Genetic programming: new performance improving methods
                 and applications",
  school =       "E{\"{o}}tv{\"{o}}s Lorand University",
  year =         "2001",
  address =      "Budapest",
  month =        "6 " # sep,
  email =        "ekart@sztaki.hu",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sztaki.hu/~ekart/th.html",
  URL =          "http://teo.elte.hu/~doktor/show_en.php?id=266",
  URL =          "http://www.inf.elte.hu/karunkrol/szolgaltatasok/konyvtar/Lists/Doktori%20disszertcik%20adatbzisa/DispForm.aspx?ID=11",
  URL =          "http://www.aston.ac.uk/EasySiteWeb/GatewayLink.aspx?alId=138898.pdf",
  size =         "103 pages",
  abstract =     "Genetic programming is the newest form of evolutionary
                 computation that was conceived in the late 1980's as a
                 possible means for automatic programming. Genetic
                 programming performs an evolutionary search in the
                 space of computer programs and selects the program that
                 solves a given task according to certain criteria. In
                 the first part of the dissertation we give an overview
                 of evolutionary computation and in particular genetic
                 programming. We raise key issues for genetic
                 programming: code growth, diversity, real world
                 applications.

                 In the second part we present our contribution to the
                 theory of genetic programming. We demonstrate two
                 methods for limiting the code growth. The first method
                 consists in applying an additional mutation operator
                 that simplifies the structure of a genetic program
                 without altering its behavior. The second method
                 applies multiobjective optimization for the objectives
                 of fitness and program size. We show that both methods
                 are successful in reducing code growth without
                 significant loss of accuracy. We then define a distance
                 metric for genetic programs and use it for applying the
                 fitness sharing technique. We propose a simple
                 diversity measure based on our metric and study the
                 effects of fitness sharing with the help of this
                 diversity measure.

                 In the third part we show the application of genetic
                 programming in two complex real world problems. The
                 first problem comes from mechanical engineering. Four
                 bar mechanisms play a very important role in practical
                 mechanism design. We describe our four bar mechanism
                 design system. We demonstrate how genetic programming
                 can be a vital component of a complex design system. We
                 integrate genetic programming with decision trees into
                 a powerful learning machine.

                 The second problem belongs to the decision support
                 domain of economics. The decision-makers have to make
                 many subjective decisions. Consequently, the final
                 decision is sensitive to even small changes in these
                 subjective values. We present our genetic programming
                 system that helps the decision-makers to arrive at
                 stable decisions. That is, for small variations in the
                 values of the involved variables, the final decision
                 remains unchanged.",
  notes =        "Supervisor: Dr. Andras Markus",
}

Genetic Programming entries for Aniko Ekart

Citations